Find Academic Supervisors

• Stochastic processes in biology
• Statistical ecology
• Population genetics
• +5 more

José Miguel Ponciano is an Associate Professor in the Department of Biology at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research bridges stochastic processes with biological questions, focusing on applications in ecology, evolution, and public health. PhD in Bioinformatics and Computational Biology, University of Idaho (2006) MS in Statistics and MS in Ecology, University of Idaho and Universidad Austral de Chile BS in Biology, Universidad del Valle de Guatemala His work spans stochastic modeling in ecology (population dynamics, community assembly), evolutionary genetics (plasmid-bacteria coevolution, antibiotic resistance), and epidemiology (anthrax transmission, vaginal microbiome dynamics). Recent projects include: NIH-funded studies on microbial community ecology NSF Dimensions of Biodiversity project on moss-microbiome interactions Multi-institutional anthrax modeling with geospatial analysis He advises students in interdisciplinary approaches, combining mathematics and biology. His lab has produced methodological advances in statistical phylogenetics (e.g., Chromploid R package) and evidential statistics (alternative to traditional hypothesis testing). Collaborations span tropical avian ecology (Peru), microbial resistance (Idaho), and public health modeling in Latin America.

Simon BonnerView profile

Associate Professor

• Ecological Statistics
• Bayesian Methods
• Mark-Recapture Models
• +7 more

Simon Bonner is an Associate Professor in Environmetrics in the Department of Statistical and Actuarial Sciences at The University of Western Ontario. Previously, he held positions at the University of Kentucky (Assistant Professor), University of British Columbia (Postdoc), and completed his PhD at Simon Fraser University. His research focuses on developing statistical models for ecological data, particularly Bayesian hierarchical methods applied to wildlife population monitoring via mark-recapture experiments. He has contributed to studies on salamanders impacted by mountaintop removal mining, predator-prey dynamics, and conservation challenges in diverse ecosystems. Education: PhD in Statistics, Simon Fraser University Postdoc, University of British Columbia Research Interests: Ecological statistics, Bayesian modeling, and hierarchical models Applications in wildlife population dynamics, conservation, and environmental impact studies Methodological advancements in capture-recapture and occupancy modeling Advising & Grants: Supervises PhD/MSc students in statistical ecology and applied data science Focus on interdisciplinary projects with collaborators in biology and environmental science Labs & Teams: Lead of the Ecological Statistics and Applied Data Science Laboratory at Western University Collaborations with researchers on salamander ecology, predator behavior, and conservation biology

Nicole LazarView profile

Professor

• Statistical Inference
• Functional Neuroimaging Data Analysis
• Model Selection
• +9 more

Nicole Lazar is the Department Head and Professor of Statistics at Pennsylvania State University. She holds a PhD from the University of Chicago (1996), an MS from Stanford University (1993), and a BA in Psychology and Statistics from Tel Aviv University (1988). Her research focuses on foundational statistical inference, neuroimaging data analysis, and topological data science, with applications in cognitive neuroscience, climatology, and psychology. She pioneered statistical methods for fMRI analysis and has developed interdisciplinary approaches at the intersection of statistics, mathematics, and computer science. Her professional service includes roles as Editor of *The American Statistician*, Associate Editor for multiple journals, and co-Editor of a Special Issue on 'Statistical Inference in the 21st Century.' Lazar authored the textbook *The Statistical Analysis of Functional MRI Data* (Springer) and has published extensively in top journals. She is a Fellow of the American Statistical Association (2014), Fellow of the Institute of Mathematical Statistics (2021), and Elected Member of the International Statistical Institute (2006). Previously, she served on faculty at Carnegie Mellon University and the University of Georgia, where she also served as Interim Department Head (2014–2016). Lazar’s teaching includes advanced courses like STAT 470W (Statistical Consulting), STAT 512 (Applied Nonparametric Statistics), and STAT 580/581 (Advanced Topics in Statistics). Her work emphasizes methodological innovations, reproducibility in science, and promoting gender equity in statistics through her presidency of the Caucus for Women in Statistics (2019).

David LandoView profile

Professor

• Credit Risk Modeling
• Financial Risk Management
• Derivatives Markets
• +3 more

David Lando is a Professor of Finance at Copenhagen Business School, affiliated with the Department of Finance and the Center for Big Data in Finance (BIGFI). He holds a Master’s degree in Mathematics-Economics from the University of Copenhagen and a PhD in Statistics from Cornell University. His research focuses on credit risk modeling, financial risk management, derivatives markets, and corporate capital structure. He authored a seminal monograph on credit risk modeling (Princeton University Press) and has published in top journals like Econometrica and the Journal of Financial Economics. Education: MSc (Math-Econ, U Copenhagen), PhD (Statistics, Cornell) Roles: Former Director of the Center for Financial Frictions (2012–2022), former Chairman of Denmark’s Financial Supervisory Authority (2018–2020), current board member of pension fund P+ His research explores liquidity dynamics in credit markets, sovereign debt relief impacts, and systemic risk. Recent work analyzes emerging market credit spreads post-debt relief and bank equity risk. His articles often bridge theoretical models with empirical validation, emphasizing practical applications for regulators and practitioners. Awards: Best Paper on Quantitative Investments (2006) He advised PhD/Master’s students and consulted for institutions like Danmarks Skibskredit and Shell. His teaching includes advanced credit risk courses, and he maintains an active role in policy through board memberships and regulatory advising. Lando leads research initiatives at CBS and collaborates with international institutions, contributing to both academic discourse and real-world financial policy.

Laurent BordesView profile

Professor

• Applied Statistics
• Reliability Engineering
• Survival Analysis
• +5 more

Laurent Bordes is a Professor at the University of Pau and the Pays de l'Adour (UPPA) , where he has held a faculty position since 2006. He earned his Doctorate in Applied Mathematics from the University of Bordeaux 1 (1996) and was granted Accreditation to Supervise Research from the University of Technology of Compiègne (2006). His academic career includes roles as Lecturer at UTC (1997-2006) and ATER at Bordeaux 1 (1996-1997). Administrative Roles : President of UPPA (2021), Director of IPRA Research Federation (2016-2017), Vice President of Research at UPPA (2012-2016), and Head of LMAP Probability and Statistics Team (2007-2008). Research Interests : Focus on survival analysis , reliability engineering , semiparametric models , gamma degradation processes , censored data , and stochastic EM algorithms . His work bridges theoretical statistics with applications in industrial reliability (e.g., aeronautics, energy systems) and biostatistics. Publications : Co-author of 15+ journal articles addressing excess hazard modeling , virtual age models , competing risks , and Monte Carlo acceleration . Collaborates with researchers in France, China, Lebanon, and Venezuela. Advising : Supervised 8 PhD students, including Gabriel Salles (gamma deterioration), Xuejing Zhao (survival analysis), and Ali Salami (degradation modeling), often with interdisciplinary co-directions and industry partnerships. Educational Contributions : Served on editorial boards for Journal of Statistical Planning and Inference , Communications in Statistics , and Journal de la Société Française de Statistique .

Raffaella GiacominiView profile

Professor

• Econometrics
• Applied Macroeconomics
• Forecasting
• +3 more

Raffaella Giacomini is a Professor of Economics at the Department of Economics, University College London. Her research focuses on econometrics, applied macroeconomics, and forecasting methodologies. She holds a PhD from the University of California and has held academic positions at UCLA and Boston College, alongside non-academic roles at the Federal Reserve Bank of Chicago. Her work emphasizes developing robust econometric methods for policy analysis and forecasting, particularly using survey data to study expectation formation. Giacomini is a Fellow of the Econometric Society and recipient of the 2024 Haavelmo Prize. She has secured grants from the European Research Council, National Science Foundation, and British Academy, and serves on editorial boards of top econometrics journals. Recent research explores subjective shocks in impulse responses, inference under narrative restrictions, and microforecasting techniques. Her applied work includes analyzing inflation dynamics and yield curve behavior, with contributions to stress testing and model uncertainty quantification. Giacomini's methodologies address challenges in structural vector autoregression, Bayesian inference under set-identification, and heterogeneous agent models. Her publications span journals like Econometrica, Journal of Econometrics, and Review of Economic Studies, with notable contributions to theory-coherent forecasting and robust Bayesian analysis. Current projects investigate inattention effects in expectation updates and tail behaviors in micropanels.

Dasha SemochkinaView profile

Research Fellow

• Computational Methods in Bayesian Statistics
• Emulation Techniques
• Bayesian Optimization
• +3 more

Dr. Dasha Semochkina is a Research Fellow at the University of Southampton's Southampton Statistical Sciences Research Institute (S3RI) and Mathematical Sciences department. Her work focuses on computational methods in Bayesian statistics, including emulator development for optimization problems and uncertainty quantification. She holds a PhD in Statistics from the University of Limerick and a bachelor's in Pure Mathematics from Lomonosov Moscow State University. Education: BSc in Pure Mathematics (Lomonosov Moscow State University), PhD in Statistics (University of Limerick). Research Interests: Emulation techniques for computationally intensive models Bayesian optimization under uncertainty Multi-objective optimization strategies Design of experiments for statistical inference Teaching: Postgraduate module MATH6174 (Likelihood and Bayesian Inference). Labs/Teams: Member of the Computational Engineering and Design Group, Statistics Research Group, and S3RI.

Rituparna SenView profile

Associate Professor

• Statistical Modeling
• Financial Data Analysis
• Risk Management
• +5 more

Rituparna Sen is an Associate Professor at the Indian Statistical Institute (ISI), Bangalore Centre. She holds a visiting role as Visiting Associate Professor at the University of California, Santa Barbara (UCSB). Her research focuses on statistical modeling, financial data analysis, risk management, and econometrics. She actively contributes to interdisciplinary fields such as portfolio optimization, Bayesian methods, and time series analysis. Education & Career: Completed PhD under Prof. Per Mykland at the University of Chicago. Serves on the faculty at ISI and teaches advanced courses like Design and Analysis of Experiments, Nonparametric Methods, and Computational Finance. Collaborates with institutions globally, including Cornell University, Columbia University, and Technische Universität München. Research Interests: Explores topics like stochastic modeling in business, financial risk assessment (e.g., spectral risk measures, Value at Risk), and applications of machine learning. Her work bridges traditional statistical methods with modern data-driven approaches, addressing challenges in high-frequency finance and market volatility. Publications & Impact: Authored over 30 peer-reviewed articles and a textbook on Computational Finance with R . Recent work includes contributions to Applied Stochastic Models in Business and Industry, Journal of Risk, and Econometrics and Statistics. Research trends emphasize interdisciplinary applications, sustainability in finance, and methodological advancements in statistical inference. Awards & Recognition: No explicit awards listed, but recognized for contributions in financial econometrics and statistical methodology. Active in academic service, including editorial roles and organizing conferences on statistical modeling. Teaching & Mentorship: Advised 5 PhD students and multiple postdocs. Teaches advanced statistical courses at ISI and UCSB, with lectures on Survival Analysis, Large Sample Theory, and Multivariate Analysis. Maintains an open-door consulting practice for statistical guidance in academia and industry. Collaborations: Leads projects with global collaborators like Prof. Claudia Klüppelberg (TUM) and Prof. Sumanta Basu (Cornell). Engages in interdisciplinary teams addressing issues like environmental finance, contagion in financial markets, and Bayesian causal analysis.

Markos KatsoulakisView profile

Professor

• Generative Modeling
• Scientific Machine Learning
• Uncertainty Quantification
• +3 more

Markos Katsoulakis is a Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, serving as Director of the Applied Mathematics & Computation program and Applied Math MS Graduate Admissions. He holds leadership roles in the Center of Applied Mathematics and the UMASS TRIPODS Institute for Theoretical Foundations of Data Science. His research focuses on generative modeling, scientific machine learning, uncertainty quantification, and multi-scale methods, with applications in materials science, catalysis, and data-driven modeling. He contributes to editorial boards of SIAM journals and teaches advanced courses like Mathematics of Generative Artificial Intelligence. Education: Ph.D. in Applied Mathematics from Brown University (1993), B.A. in Mathematics from National and Kapodistrian University of Athens (1987). Research emphasizes foundational mathematics in generative AI, including trustworthiness, robustness, and integration with physical models. Recent work addresses high-dimensional control, likelihood-free inference, and generative particle algorithms for scarce data. His publications span machine learning, data science, and applied mathematics, with a focus on theoretical guarantees and algorithmic innovation. Leadership includes directing interdisciplinary initiatives and mentoring through the Applied Mathematics program. His work bridges computational science and data-driven methodologies, emphasizing model correctability and uncertainty quantification in complex systems.

Yuning LiView profile

Lecturer

• Financial Econometrics
• Statistical Modelling
• High-Dimensional Data Analysis
• +3 more

Dr. Yuning Li is a Lecturer in Accounting and Finance at the School for Business and Society, University of York, since September 2023. He holds a BSc in Mathematics and a PhD in Statistics from Zhejiang University, as well as a PhD in Economics from the University of York. His research focuses on financial econometrics and statistical modelling, emphasizing high-dimensional data analysis and financial data applications. Dr. Li's education includes: Bachelor of Science in Mathematics, Zhejiang University Doctor of Philosophy in Statistics, Zhejiang University Doctor of Philosophy in Economics, University of York His research interests encompass financial econometrics, particularly time series analysis, market microstructure, and high-frequency data, alongside statistical methodologies such as nonparametric estimation, change point detection, and high-dimensional data analysis. His work often involves developing innovative statistical models and applying them to real-world financial datasets, with a current emphasis on latent factor models for dimension reduction and portfolio management. His publications span topics including structural break detection in large covariance matrices, efficient price estimation in high-frequency models, and methodological advancements in expectile regression and nonparametric analysis. These contributions reflect his expertise in both theoretical statistics and applied financial econometrics. Dr. Li has taught courses such as Mathematics 2, Financial Economics and Capital Markets, and Quantitative Methods in Finance at the University of York. He currently oversees courses in financial modelling and individual research projects for finance students. No scientific awards or grants are explicitly listed in the provided information. His academic contributions are primarily through his research output and teaching engagements.

Francesco BravoView profile

Associate Professor

• Econometric Theory
• Statistical Theory
• Nonparametric Methods
• +3 more

Dr. Francesco Bravo is a Reader in the Department of Economics at the University of York. His research focuses on econometric theory, statistical theory, and applied econometrics, with particular emphasis on nonparametric and semiparametric methods, higher-order asymptotic theory, and missing data analysis. He holds a Laurea from the University of Udine, an MSc from the University of Southampton, and a PhD from the University of Southampton. His research interests include nonparametric likelihood, generalized empirical likelihood methods, and the development of robust statistical techniques for complex models. He has contributed to advancements in econometric methods for time series and panel data analyses, particularly in handling dependent data structures and model misspecification. Dr. Bravo has published widely in top-tier journals such as the Annals of the Institute of Statistical Mathematics , Journal of Time Series Analysis , and Statistics & Probability Letters . His work often bridges theoretical contributions with practical applications, addressing challenges in economic and health-related data analysis. He has secured research funding, including a project on Sieve non-parametric methods for unit root tests funded by the Economic and Social Research Council (ESRC). He teaches undergraduate courses in Probability and Statistics, Econometric Methods for Research, and graduate-level econometrics. His office hours are Wednesday 11:00-13:00, and he can be reached at francesco.bravo@york.ac.uk for appointments.

Peter BeerliView profile

Professor

• Population Genetics
• Phylogenetics
• Biological Sciences
• +3 more

Peter Beerli is a Professor and Chair of the Department of Scientific Computing at Florida State University (FSU). He specializes in computational evolutionary biology, population genetics, and phylogenetics, developing software tools such as MIGRATE to analyze genetic data. His research focuses on coalescent theory, Bayesian inference, and model selection. Education: PhD in Zoology from the University of Zurich (1994), followed by postdoctoral work with Joseph Felsenstein at the University of Washington. His career spans computational biology, starting from evolutionary studies in amphibians to software development for population genetic analysis. Research highlights include the fractional coalescent model (f-coalescent), divergence time estimation, and integrating epidemiological data with phylogenetics. He teaches courses in algorithms, scientific computing, and population genetics. His lab members include Haleh Ashki, Somayeh Mashayekhi, and Kyle Shaw, among others. Beerli's work emphasizes applying mathematical and computational methods to address biological questions, with contributions to malaria parasite evolution, whale population studies, and pathogen transmission modeling. His software MIGRATE is widely used for inferring migration rates and population sizes.

Nina Therese DörnemannView profile

Assistant Professor

• High-Dimensional Statistics
• Random Matrix Theory
• Asymptotic Statistics
• +3 more

Nina Therese Dörnemann is a Tenure-Track Assistant Professor at the Department of Mathematics, Aarhus University since 2024. Previously, she was a Visiting Assistant Professor at the Department of Statistics, University of California, Davis, and a Postdoctoral Researcher at the Faculty of Mathematics, Ruhr University Bochum. She earned her PhD in 2022 under the supervision of Holger Dette at Ruhr University Bochum. Her research focuses on high-dimensional statistics and random matrix theory, with broader interests in asymptotic statistics, hypothesis testing, and resampling techniques in high-dimensional contexts. Her mission is to develop mathematically rigorous tools for high-dimensional inference, particularly by understanding universal properties of large-dimensional random matrices. Her publications include works on detecting change points in covariance matrices, spectral breaks in spiked covariance models, and fluctuations in sample precision matrices. These contributions highlight her expertise in statistical methodology and theoretical foundations of high-dimensional data analysis. Dr. Dörnemann’s work is supported by research grants, though specific details are not provided in the profile. She is affiliated with the Department of Mathematics at Aarhus University and maintains a professional network through collaborations with institutions like Ruhr University Bochum and UC Davis.

Ayşen Dener AkkayaView profile

Professor

• Time series analysis
• Stochastic processes
• Risk and decision analysis
• +3 more

Prof. Ayşen Dener Akkaya is a Professor in the Department of Statistics at Middle East Technical University (METU), where she has held academic positions since 1985. She earned her Ph.D. in Statistics from METU in 1995, following her M.Sc. (1987) and B.Sc. (1985) in the same discipline. Her professional experience includes a Visiting Fellowship at Princeton University's Department of Civil Engineering and Operations Research (1997–1998). Her research focuses on time series analysis , stochastic processes , and risk analysis , with applications in earthquake engineering, seismic hazard modeling, and robust statistical methods. Key areas include autoregressive models, parameter estimation under non-normal conditions, and stochastic modeling of geotechnical systems. Her publications span journals like Journal of Computational and Applied Mathematics and Structural Safety , emphasizing robust statistical techniques and their engineering applications. She co-authored the influential textbook Robust Estimation and Hypothesis Testing (2004), which remains a key reference in statistical methodology.

Moreno BevilacquaView profile

Associate Professor

• Spatial Statistics
• Covariance Modeling
• Geostatistics
• +3 more

Moreno Bevilacqua is an Associate Professor in the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on spatial statistics, covariance modeling, and geostatistical methods, with applications in environmental and multivariate analysis. He has contributed to advancing covariance functions, composite likelihood approaches, and spatial data modeling techniques. Recent publications emphasize statistical methodologies for spatial and spatiotemporal data, including extensions of the Matérn model, composite likelihood for massive datasets, and copula-based approaches for bounded support data. His work bridges theoretical advancements and practical applications in environmental science and statistical computing. No scientific awards or grants are explicitly mentioned in the provided texts. Teaching activities include courses in statistics for undergraduate programs in Business Economics, covering foundational and advanced statistical methods. Current office location: Assegnisti A1 office (Zeta A building, 1st floor), Campus via Torino.